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一致net NaiveBayesLearning“索引超出数组界限”

accord.net .net-core c#

ConductedClever · 技术社区 · 9 年前

我正在使用Accord。net 3.7.0在dot net core 1.1中。

我使用的算法是朴素贝叶斯。学习机制的源代码如下:

    public LearningResultViewModel NaiveBayes(int[][] inputs, int[] outputs)
    {
        // Create a new Naive Bayes learning
        var learner = new NaiveBayesLearning();

        // Learn a Naive Bayes model from the examples
        NaiveBayes nb = learner.Learn(inputs, outputs);

        #region test phase
        // Compute the machine outputs
        int[] predicted = nb.Decide(inputs);

        // Use confusion matrix to compute some statistics.
        ConfusionMatrix confusionMatrix = new ConfusionMatrix(predicted, outputs, 1, 0);
        #endregion

        LearningResultViewModel result = new LearningResultViewModel()
        {
            Distributions = nb.Distributions,
            NumberOfClasses = nb.NumberOfClasses,
            NumberOfInputs = nb.NumberOfInputs,
            NumberOfOutputs = nb.NumberOfOutputs,
            NumberOfSymbols = nb.NumberOfSymbols,
            Priors = nb.Priors,
            confusionMatrix = confusionMatrix
        };

        return result;
    }

发生错误。

因为我无法在 Learn

没有额外的信息,没有内在的例外,没有想法!!!

TG。

//更新_1***

每行有4列。手工检查(如果需要,我也可以分享它的视频!!!)

输出阵列为180阵列,如下所示:

NaiveBayesinLearning

本页底部有更多示例:

More examples

和 learn

learn method doc

1 回复 | 直到 9 年前

ConductedClever 9 年前

根据他们的评论和想法,我怀疑矩阵的价值。所以我调查了一下:

如上图所示,一些行的值低于零值。输入矩阵是通过编码生成的,在这里的示例中使用:

NaiveBayes

Codification docs

编码-1的值为null。如下图所示:

所以我的解决方案是替换 null "null" . 但可能有更好的解决方案。

现在,包含固定数据的调用方方法如下所示:

    public LearningResultViewModel Learn(EMVDBContext dBContext, string userId, LearningAlgorithm learningAlgorithm)
    {
        var learningDataRaw = dBContext.Mutants
            .Include(mu => mu.MutationOperator)
            .Where(mu => mu.Equivalecy == 0 || mu.Equivalecy == 10);

        string[] featureTitles = new string[] {
        "ChangeType",
        "OperatorName",
        "OperatorBefore",
        "OperatorAfter",
        };

        string[][] learningInputNotCodified = learningDataRaw.Select(ldr => new string[] {
            ldr.ChangeType.ToString(),
            ldr.MutationOperator.Name??"null",
            ldr.MutationOperator.Before??"null",
            ldr.MutationOperator.After??"null",
        }).ToArray();

        int[] learningOutputNotCodified = learningDataRaw.Select(ldr => ldr.Equivalecy == 0 ? 0 : 1).ToArray();

        #region Codification phase
        // Create a new codification codebook to
        // convert strings into discrete symbols
        Codification codebook = new Codification(featureTitles, learningInputNotCodified);

        // Extract input and output pairs to train
        int[][] learningInput = codebook.Transform(learningInputNotCodified);

        switch (learningAlgorithm)
        {
            case LearningAlgorithm.NaiveBayesian:
                return learningService.NaiveBayes(learningInput, learningOutputNotCodified);
                break;
            case LearningAlgorithm.SVM:
                break;
            default:
                break;
        }
        #endregion

        return null;
    }